The role of geography and distance on physician follow-up after a first hospitalization with a diagnosis of a schizophrenia spectrum disorder: A retrospective population-based cohort study in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Timely follow-up after hospitalization for a schizophrenia spectrum disorder (SSD) is an important quality indicator. We examined the proportion of individuals who received physician follow-up within 7 and 30 days post-discharge by health region and estimated the effect of distance between a person's residence and discharging hospital on follow-up. METHODS: We created a retrospective population-based cohort of incident hospitalizations with a discharge diagnosis of a SSD between 01/01/2012 and 30/03/2019. The proportion of follow-up with a psychiatrist and family physician within 7 and 30 days were calculated for each region. The effect of distance between a person's residence and discharging hospital on follow-up was estimated using adjusted multilevel logistic regression models. RESULTS: We identified 6,382 incident hospitalizations for a SSD. Only 14.2% and 49.2% of people received follow-up care with a psychiatrist within 7 and 30 days of discharge, respectively, and these proportions varied between regions. Although distance from hospital was not associated with follow-up within 7 days of discharge, increasing distance was associated with lower odds of follow-up with a psychiatrist within 30 days. CONCLUSION: Post-discharge follow-up is poor across the province. Geospatial factors may impact post-discharge care and should be considered in further evaluation of quality of care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".